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20242026
most citedExploring Multi-Lingual Bias of Large Code Models in Code Generation

2 citations · 3 across the 7 of their papers we have counts for

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7 papers

cs.AI2026

Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale

Jia Li, Wenyuan Ma, Ting Peng +2

Diagnosing mobile crashes in ultra-large-scale industrial applications is a formidable challenge due to the sheer volume of code, the complexity of mixed-language environments, and…

cs.AI2026

Signal Reshaping for GRPO in Weak-Feedback Agentic Code Repair

Jia Li, Yuxin Su, Ting Peng +3

Code-agent RL often receives weak feedback: rollout-time signals are reliable and executable, but capture only necessary or surface conditions for task success rather than the targ…

cs.SE2026

Cascaded Code Editing: Large-Small Model Collaboration for Effective and Efficient Code Editing

Chaozheng Wang, Zezhou Yang, Shuzheng Gao +7

Code editing constitutes a fundamental practice in software development, wherein developers modify existing codebases according to natural language requirements. Accurate code edit…

cs.CL2026

Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage

Junhao Hu, Fangze Li, Mingtao Xu +9

Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference e…

cs.SE2025★ 1 cited

A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat

Zezhou Yang, Ting Peng, Cuiyun Gao +3

Code completion, a crucial task in software engineering that enhances developer productivity, has seen substantial improvements with the rapid advancement of large language models…

cs.SE2025

RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industry

Chaozheng Wang, Zezhou Yang, Shuzheng Gao +5

Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the e…

cs.SE2024★ 2 cited

Exploring Multi-Lingual Bias of Large Code Models in Code Generation

Chaozheng Wang, Zongjie Li, Cuiyun Gao +6

Code generation aims to synthesize code and fulfill functional requirements based on natural language (NL) specifications, which can greatly improve development efficiency. In the…